Tootfinder

Onsager Principle-Based Domain Embedding for Thermodynamically Consistent Cahn-Hilliard Model in Arbitrary Domain
Wenkai Yu, Qi Wang, Zhen Zhang, Tiezheng Qian
https://arxiv.org/abs/2508.06830

Onsager Principle-Based Domain Embedding for Thermodynamically Consistent Cahn-Hilliard Model in Arbitrary Domain
The original Cahn-Hilliard model in an arbitrary domain with two prescribed boundary conditions is extended to a Cahn-Hilliard-type model in a larger, regular domain with homogeneous Neumann boundary conditions. The extension is based on the Onsager principle-based domain embedding (OPBDE) method, which has been developed as a systematic domain embedding framework to ensure thermodynamic consistency. By introducing a modified conservation law, the flux at the boundary of the original domain is …

Compass-v3: Scaling Domain-Specific LLMs for Multilingual E-Commerce in Southeast Asia
Sophia Maria
https://arxiv.org/abs/2509.09121 https://arxiv.org/pdf/…

Compass-v3: Scaling Domain-Specific LLMs for Multilingual E-Commerce in Southeast Asia
Large language models (LLMs) excel in general-domain applications, yet their performance often degrades in specialized tasks requiring domain-specific knowledge. E-commerce is particularly challenging, as its data are noisy, heterogeneous, multilingual, and highly dynamic. We present Compass-v3, a vertical-domain Mixture-of-Experts (MoE) model with 245B total parameters and 71B active per token, designed for Southeast Asian e-commerce. Compass-v3 adopts fewer but larger experts, combined with h…

interactome_pdz: PDZ-domain interactome (2005)
A network of PDZ-domain-mediated protein–protein binding interactions, extracted from the PDZBase database. Nodes represent proteins and an edge represents a binding interaction between two proteins.
This network has 212 nodes and 244 edges.
Tags: Biological, Protein interactions, Unweighted

TRUST: Leveraging Text Robustness for Unsupervised Domain Adaptation
Mattia Litrico, Mario Valerio Giuffrida, Sebastiano Battiato, Devis Tuia
https://arxiv.org/abs/2508.06452 ht…

TRUST: Leveraging Text Robustness for Unsupervised Domain Adaptation
Recent unsupervised domain adaptation (UDA) methods have shown great success in addressing classical domain shifts (e.g., synthetic-to-real), but they still suffer under complex shifts (e.g. geographical shift), where both the background and object appearances differ significantly across domains. Prior works showed that the language modality can help in the adaptation process, exhibiting more robustness to such complex shifts. In this paper, we introduce TRUST, a novel UDA approach that exploit…

Blockchain-Based Decentralized Domain Name System
Guang Yang, Peter Trinh, Alma Nkemla, Amuru Serikyaku, Edward Tatchim, Osman Sharaf
https://arxiv.org/abs/2508.05655 https://…

Blockchain-Based Decentralized Domain Name System
The current Domain Name System (DNS) infrastructure faces critical vulnerabilities including poisoning attacks, censorship mechanisms, and centralized points of failure that compromise internet freedom and security. Recent incidents such as DNS poisoning attacks on ISP customers highlight the urgent need for resilient alternatives. This paper presents a novel blockchain-based Decentralized Domain Name System (DDNS). We designed a specialized Proof-of-Work blockchain to maximize support for DNS-…

Towards Adaptive ML Benchmarks: Web-Agent-Driven Construction, Domain Expansion, and Metric Optimization
Hangyi Jia, Yuxi Qian, Hanwen Tong, Xinhui Wu, Lin Chen, Feng Wei
https://arxiv.org/abs/2509.09321

Towards Adaptive ML Benchmarks: Web-Agent-Driven Construction, Domain Expansion, and Metric Optimization
Recent advances in large language models (LLMs) have enabled the emergence of general-purpose agents for automating end-to-end machine learning (ML) workflows, including data analysis, feature engineering, model training, and competition solving. However, existing benchmarks remain limited in task coverage, domain diversity, difficulty modeling, and evaluation rigor, failing to capture the full capabilities of such agents in realistic settings. We present TAM Bench, a diverse, realistic, and st…

the safety advice following the recent npm phishing attacks reminds me of an old rant https://dotat.at/@/2016-10-19-domain-transfers-are-shocking.html
i hope things have improved since then (happily i have not needed to do any domain transfers recently)…

https://therecord.media/angus-king-cyber-domain-cuts-cisa
Sen. Angus King (I-ME) on Tuesday said that recent cuts across the government have weakened U.S. cybersecurity even as attacks against infrastructure and business skyrocket.

Sen. King: Cyber domain is a ‘hellscape’ that will be made worse by cuts
King, a leading voice in the Senate on cybersecurity issues, honed in on the thousands of staffers and experts laid off by CISA, saying the agency has lost 30 percent of its staff and most of its seasoned leaders.

LMAR: Language Model Augmented Retriever for Domain-specific Knowledge Indexing
Yao Zhao, Yantian Ding, Zhiyue Zhang, Dapeng Yao, Yanxun Xu
https://arxiv.org/abs/2508.05672 http…

LMAR: Language Model Augmented Retriever for Domain-specific Knowledge Indexing
Retrieval Augmented Generation (RAG) systems often struggle with domain-specific knowledge due to performance deterioration of pre-trained embeddings and prohibitive computational costs of large language model (LLM)-based retrievers. While fine-tuning data augmentation embedding models offers a promising direction, its effectiveness is limited by the need for high-quality training data and reliable chunking strategies that preserve contextual integrity. We propose LMAR (Language Model Augmented…

I’m interested in seeing if more folks end up using the new #dotnet 10 domain name feature for local development. So often, I see mistakes that essentially creep in because the domain and app topology don’t match those of staging and production environments.
I wish there was better domain name tools for #dotnet

Physical Design Exploration of a Wire-Friendly Domain-Specific Processor for Angstrom-Era Nodes
Lorenzo Ruotolo, Lara Orlandic, Pengbo Yu, Moritz Brunion, Daniele Jahier Pagliari, Dwaipayan Biswas, Giovanni Ansaloni, David Atienza, Julien Ryckaert, Francky Catthoor, Yukai Chen
https://arxiv.org/abs/2508.07110…

Physical Design Exploration of a Wire-Friendly Domain-Specific Processor for Angstrom-Era Nodes
This paper presents the physical design exploration of a domain-specific processor (DSIP) architecture targeted at machine learning (ML), addressing the challenges of interconnect efficiency in advanced Angstrom-era technologies. The design emphasizes reduced wire length and high core density by utilizing specialized memory structures and SIMD (Single Instruction, Multiple Data) units. Five configurations are synthesized and evaluated using the IMEC A10 nanosheet node PDK. Key physical design m…

The Consequences of Rubin Observatory Time-Domain Survey Design and Host-Galaxy Contamination on the Identification of Binary Supermassive Black Holes
Megan C. Davis, Jonathan R. Trump, Maria Charisi, Jessie C. Runnoe, W. N. Brandt, Kaylee E. Grace, London E. Willson
https://arxiv.org/abs/2508.05742

The Consequences of Rubin Observatory Time-Domain Survey Design and Host-Galaxy Contamination on the Identification of Binary Supermassive Black Holes
Binary supermassive black holes (SMBHs) are consequences of galaxy mergers and dominate the low-frequency gravitational wave background. Finding binary SMBHs in existing time-domain observations has proven difficult, as their periodic, electromagnetic signals can be confused with the natural variability of single quasars. In this work, we investigate the effects of host-galaxy contamination and survey design (cadence and duration) on the detectability of binary SMBHs with the upcoming Rubin Obs…

Cross-Domain Evaluation of Transformer-Based Vulnerability Detection on Open & Industry Data
Moritz Mock, Thomas Forrer, Barbara Russo
https://arxiv.org/abs/2509.09313 https…

Cross-Domain Evaluation of Transformer-Based Vulnerability Detection on Open & Industry Data
Deep learning solutions for vulnerability detection proposed in academic research are not always accessible to developers, and their applicability in industrial settings is rarely addressed. Transferring such technologies from academia to industry presents challenges related to trustworthiness, legacy systems, limited digital literacy, and the gap between academic and industrial expertise. For deep learning in particular, performance and integration into existing workflows are additional concer…

DETACH: Cross-domain Learning for Long-Horizon Tasks via Mixture of Disentangled Experts
Yutong Shen, Hangxu Liu, Penghui Liu, Ruizhe Xia, Tianyi Yao, Yitong Sun, Tongtong Feng
https://arxiv.org/abs/2508.07842

DETACH: Cross-domain Learning for Long-Horizon Tasks via Mixture of Disentangled Experts
Long-Horizon (LH) tasks in Human-Scene Interaction (HSI) are complex multi-step tasks that require continuous planning, sequential decision-making, and extended execution across domains to achieve the final goal. However, existing methods heavily rely on skill chaining by concatenating pre-trained subtasks, with environment observations and self-state tightly coupled, lacking the ability to generalize to new combinations of environments and skills, failing to complete various LH tasks across do…

Two Sides of the Same Optimization Coin: Model Degradation and Representation Collapse in Graph Foundation Models
Xunkai Li, Daohan Su, Sicheng Liu, Ru Zhang, Rong-Hua Li, Guoren Wang
https://arxiv.org/abs/2509.08401

Two Sides of the Same Optimization Coin: Model Degradation and Representation Collapse in Graph Foundation Models
Graph foundation models, inspired by the success of LLMs, are designed to learn the optimal embedding from multi-domain TAGs for the downstream cross-task generalization capability. During our investigation, graph VQ-MAE stands out among the increasingly diverse landscape of GFM architectures. This is attributed to its ability to jointly encode topology and textual attributes from multiple domains into discrete embedding spaces with clear semantic boundaries. Despite its potential, domain gener…

Stability and bifurcation of Navier-Stokes equations in an annular domain with mixed boundary conditions
Zhibo Hou, Liang Li, Quan Wang
https://arxiv.org/abs/2508.07694 https://…

Stability and bifurcation of Navier-Stokes equations in an annular domain with mixed boundary conditions
We study the existence and stability of non-trivial steady-state solutions to the two-dimensional incompressible Navier-Stokes equations in an annular domain $Ω= B(0,b) \setminus \overline{B(0,a)}$ with radii $b>a>0$.The outer boundary $\partial B(0, b)$ is subject to the free condition, while the inner boundary $\partial B(0, a)$ obeys a Navier-slip condition with effective slip length $α> 0$. Our main results are fourfold. First, we establish global-in-time strong solutions and derive a sha…

DogFit: Domain-guided Fine-tuning for Efficient Transfer Learning of Diffusion Models
Yara Bahram, Mohammadhadi Shateri, Eric Granger
https://arxiv.org/abs/2508.05685 https://…

DogFit: Domain-guided Fine-tuning for Efficient Transfer Learning of Diffusion Models
Transfer learning of diffusion models to smaller target domains is challenging, as naively fine-tuning the model often results in poor generalization. Test-time guidance methods help mitigate this by offering controllable improvements in image fidelity through a trade-off with sample diversity. However, this benefit comes at a high computational cost, typically requiring dual forward passes during sampling. We propose the Domain-guided Fine-tuning (DogFit) method, an effective guidance mechanis…

Feynman-Kac formula gor general time dependent stochastic parabolic equation on a bounded domain and applications
Yaozhong Hu, Qun Shi
https://arxiv.org/abs/2508.07793 https://

Feynman-Kac formula gor general time dependent stochastic parabolic equation on a bounded domain and applications
This paper establishes a Feynman-Kac formula to represent the solution to general time inhomogeneous stochastic parabolic partial differential equations driven by multiplicative fractional Gaussian noises in bounded domain where L_t is a second order uniformly elliptic operator whose coefficients can depend on time and generates a time inhomoegenous Markov process. The idea is to use the Aronson bounds of fundamental solution of the associated heat kernel and the techniques from Malliavin calcu…

Frequency-Domain Analysis of Time-Dependent Multiomic Data in Progressive Neurodegenerative Diseases: A Proposed Quantum-Classical Hybrid Approach with Quaternionic Extensions
John D. Mayfield M. D. Ph. D. M. Sc
https://arxiv.org/abs/2508.07948

Frequency-Domain Analysis of Time-Dependent Multiomic Data in Progressive Neurodegenerative Diseases: A Proposed Quantum-Classical Hybrid Approach with Quaternionic Extensions
Progressive neurodegenerative diseases, including Alzheimer's disease (AD), multiple sclerosis (MS), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS), exhibit complex, nonlinear trajectories that challenge deterministic modeling. Traditional time-domain analyses of multiomic and neuroimaging data often fail to capture hidden oscillatory patterns, limiting predictive accuracy. We propose a theoretical mathematical framework that transforms time-series data into frequency or s-do…

LADB: Latent Aligned Diffusion Bridges for Semi-Supervised Domain Translation
Xuqin Wang, Tao Wu, Yanfeng Zhang, Lu Liu, Dong Wang, Mingwei Sun, Yongliang Wang, Niclas Zeller, Daniel Cremers
https://arxiv.org/abs/2509.08628

LADB: Latent Aligned Diffusion Bridges for Semi-Supervised Domain Translation
Diffusion models excel at generating high-quality outputs but face challenges in data-scarce domains, where exhaustive retraining or costly paired data are often required. To address these limitations, we propose Latent Aligned Diffusion Bridges (LADB), a semi-supervised framework for sample-to-sample translation that effectively bridges domain gaps using partially paired data. By aligning source and target distributions within a shared latent space, LADB seamlessly integrates pretrained source…

A classification of Prufer domains of integer-valued polynomials on algebras
Giulio Peruginelli, Nicholas J. Werner
https://arxiv.org/abs/2509.09243 https://

A classification of Prufer domains of integer-valued polynomials on algebras
Let $D$ be an integrally closed domain with quotient field $K$ and $A$ a torsion-free $D$-algebra that is finitely generated as a $D$-module and such that $A\cap K=D$. We give a complete classification of those $D$ and $A$ for which the ring $\text{Int}_K(A)=\{f\in K[X] \mid f(A)\subseteq A\}$ is a Prüfer domain. If $D$ is a semiprimitive domain, then we prove that $\text{Int}_K(A)$ is Prüfer if and only if $A$ is commutative and isomorphic to a finite direct product of almost Dedekind domain…

The number of countable models of first-order theories
Anand Pillay, Predrag Tanovi\'c
https://arxiv.org/abs/2508.06854 https://arxiv.org/pdf/2508.0685…

The number of countable models of first-order theories
Throughout, $T$ denotes a complete first-order theory in a countable language $L$ that has infinite models and $I(\aleph_0,T)$ denotes the number of countable models of $T$, up to an isomorphism. To determine $I(\aleph_0,T)$, it suffices to consider only countable models of $T$ with domain $ω$; since there are at most continuum many $L$-structures with domain $ω$, $I(\aleph_0,T)\leqslant 2^{\aleph_0}$ holds. Theories with $I(\aleph_0,T)=1$ are the $\aleph_0$-categorical theories. These includ…

Low-Complexity Equalization of Zak-OTFS in the Frequency Domain
Sandesh Rao Mattu, Nishant Mehrotra, Saif Khan Mohammed, Venkatesh Khammammetti, Robert Calderbank
https://arxiv.org/abs/2508.07148

Low-Complexity Equalization of Zak-OTFS in the Frequency Domain
4G/5G wireless standards use orthogonal frequency division multiplexing (OFDM) which is robust to frequency selectivity. Equalization is possible with a single tap filter, and low-complexity equalization makes OFDM an attractive physical layer. However the performance of OFDM degrades with mobility, since Doppler spreads introduce inter-carrier interference (ICI) between subcarriers and they are no longer orthogonal. Zak-transform based orthogonal time frequency space (Zak-OTFS) modulation has …

from my link log —
What declarative languages are.
https://semantic-domain.blogspot.com/2013/07/what-declarative-languages-are.html
saved 2025-08-01

SCA-LLM: Spectral-Attentive Channel Prediction with Large Language Models in MIMO-OFDM
Ke He, Le He, Lisheng Fan, Xianfu Lei, Thang X. Vu, George K. Karagiannidis, Symeon Chatzinotas
https://arxiv.org/abs/2509.08139

SCA-LLM: Spectral-Attentive Channel Prediction with Large Language Models in MIMO-OFDM
In recent years, the success of large language models (LLMs) has inspired growing interest in exploring their potential applications in wireless communications, especially for channel prediction tasks. However, directly applying LLMs to channel prediction faces a domain mismatch issue stemming from their text-based pre-training. To mitigate this, the ``adapter + LLM" paradigm has emerged, where an adapter is designed to bridge the domain gap between the channel state information (CSI) data and …

The whole domain registration industry is a cesspit of rentier crooks working for the benefit of rentier crooks. Including ICANN.
It was a mistake to end the InterNIC.
https://social.growyourown.services/@FediTips/115167305897549859

Toward a Multi-Echelon Cyber Warfare Theory: A Meta-Game-Theoretic Paradigm for Defense and Dominance
Ya-Ting Yang, Quanyan Zhu
https://arxiv.org/abs/2509.08976 https://

Toward a Multi-Echelon Cyber Warfare Theory: A Meta-Game-Theoretic Paradigm for Defense and Dominance
Cyber warfare has become a central element of modern conflict, especially within multi-domain operations. As both a distinct and critical domain, cyber warfare requires integrating defensive and offensive technologies into coherent strategies. While prior research has emphasized isolated tactics or fragmented technologies, a holistic understanding is essential for effective resource deployment and risk mitigation. Game theory offers a unifying framework for this purpose. It not only models atta…

Straightforward Bayesian A/B testing with Dirichlet posteriors
Dustin Hayden, Thomas Armitage
https://arxiv.org/abs/2508.08077 https://arxiv.org/pdf/2508.0…

Straightforward Bayesian A/B testing with Dirichlet posteriors
Bayesian A/B testing investigates metric changes using the joint posterior distribution of two (or more) experimentally-derived datasets. The construction of said joint posterior is often a time-consuming process requiring specialized knowledge and domain expertise. In businesses that perform tens to hundreds of A/B tests per month it is important to have a robust analysis pipeline that can handle the variety of experiments performed on a modern web platform; requiring a domain expert to select…

#PSA #feditips It seems that Nepal has blocked access to (some?) Mastodon sites.
With domain fronting the blocking of many fediverse sites can be circumvented.
This is based on the fact that many websites share the same IP number. Therefore a government blocking access to a website can in many cases only block the domain, but not the IP of the site, otnerwise many other websites would be blocked too.
By either configuring a proxy in the mastodon app or on the device or by usin…

#PSA #feditips It seems that Nepal has blocked access to (some?) Mastodon sites.
With domain fronting the blocking of many fediverse sites can be circumvented.
This is based on the fact that many websites share the same IP number. Therefore a government blocking access to a website can in many cases only block the domain, but not the IP of the site, otnerwise many other websites would be blocked too.
By either configuring a proxy in the mastodon app or on the device or by usin…

Weekend #Plankton Factoid 🦠🦐
For a while, there has been concern for how the ocean's #ecosystem is being modeled. Too often it is oversimplified with #phytoplankon and

What do you mean that someone can buy your domain 12 days before it expires?
It happened to gup.pe who runs the group system a.gup.pe
Their domain was supposed to expire on the 15th, but the domain is already been bought by someone else to host spam articles.
According to Namecheap, there is no renewal period and no pendingDelete period, so customers must renew before the last 12 days of the domain, or it can be bought instantly within that 12-day period.

Sen. King: Cyber domain is a ‘hellscape’ that will be made worse by cuts https://therecord.media/angus-king-cyber-domain-cuts-cisa

"We think of generating source code from a prompt as an AI-powered feature of modern IDEs, but the general problem has a rich history in research efforts and domain-specific programming systems." At Berlin Buzzwords 2025, William Benton discussed the history of program synthesis, its relationship to the history of AI, and the lessons we can learn from it today.
Watch the full session: #bbuzz 2026: https://tickets.plainschwarz.com/bbuzz26/c/8Hvk0ZvJA/

OmniBench-RAG: A Multi-Domain Evaluation Platform for Retrieval-Augmented Generation Tools
Jiaxuan Liang, Shide Zhou, Kailong Wang
https://arxiv.org/abs/2508.05650 https://

OmniBench-RAG: A Multi-Domain Evaluation Platform for Retrieval-Augmented Generation Tools
While Retrieval Augmented Generation (RAG) is now widely adopted to enhance LLMs, evaluating its true performance benefits in a reproducible and interpretable way remains a major hurdle. Existing methods often fall short: they lack domain coverage, employ coarse metrics that miss sub document precision, and fail to capture computational trade offs. Most critically, they provide no standardized framework for comparing RAG effectiveness across different models and domains. We introduce OmniBenc…

THz-PINNs: Time-Domain Forward Modeling of Terahertz Spectroscopy with Physics-Informed Neural Networks
Pengfei Zhu, Xavier Maldague
https://arxiv.org/abs/2509.07161 https://

THz-PINNs: Time-Domain Forward Modeling of Terahertz Spectroscopy with Physics-Informed Neural Networks
Terahertz time-domain spectroscopy (THz-TDS) is a powerful tool for extracting optical and electrical parameters, as well as probing internal structures and surface morphology of materials. However, conventional simulation techniques, such as the finite element method (FEM) and finite-difference time-domain (FDTD), face challenges in accurately modeling THz wave propagation. These difficulties arise from the broad operating bandwidth of THz-TDS (~0.1-10 THz), which demands extremely high tempor…

This year at #CCN2025 we will be showcasing our research on the classification of Mental Workload 🥵 Spatial Effects using Riemannian Manifold.
📅 When: Wednesday, August 13, 1:00 – 4:00 pm
📍 Where: CCN 2025 Conference Venue, de Brug & E-Hall
📋 What: Poster B152

• It leverages advanced mathematical techniques to better understand and classify mental workloads, offerin…

Classification of Mental Workload Spatial Effects using Riemannian Manifold | Next-generation neural computations
This study investigates the use of Riemannian geometry to classify mental workload from an EEG dataset collected in an aeronautical context. The analysis, based on EEG data recorded from 16 participants performing a Simon task, aimed to differentiate low and high workload conditions. Using covariance matrices and a Minimum Distance to Mean (MDM) classifier, the results demonstrate spatial effects of mental workload irrespective of the investigated spectral domain. This demonstrates that spatial…

Uncovering domain morphology in an unconventional magnet with scanning diamond quantum magnetometry
Freya Johnson (Cavendish Laboratory, University of Cambridge), Jan Zemen (Faculty of Electrical Engineering, Czech Technical University in Prague), Helena Knowles (Cavendish Laboratory, University of Cambridge), Lesley F. Cohen (Department of Physics, Blackett Laboratory, Imperial College London)

Uncovering domain morphology in an unconventional magnet with scanning diamond quantum magnetometry
Unconventional magnetic materials including non-collinear antiferromagnets, p-wave magnets and altermagnets, are an emerging frontier for quantum spintronics and hybrid quantum devices. Critical to the application of these materials is control over the magnetic domain state, as their unique, symmetry-driven properties vanish in a multi-domain limit. However, the mechanisms governing domain formation in materials with compensated local moments remain poorly understood. In this work, we examine t…

On the Lebesgue Constant of Extended-Domain Spectral Methods for Elliptic PDEs
Po-Yi Wu
https://arxiv.org/abs/2509.08745 https://arxiv.org/pdf/2509.08745…

On the Lebesgue Constant of Extended-Domain Spectral Methods for Elliptic PDEs
The extended-domain method is an appealingly simple strategy for applying spectral methods to complex geometries, but its theoretical stability properties, particularly for non-normal operators, are not fully understood. This paper provides a rigorous stability analysis based on the Lebesgue constant and reveals a fundamental stability dichotomy at the heart of the method. We first prove a surprising result: for the self-adjoint Poisson equation, the method is unstable, with a Lebesgue constant…

SciNLP: A Domain-Specific Benchmark for Full-Text Scientific Entity and Relation Extraction in NLP
Decheng Duan, Yingyi Zhang, Jitong Peng, Chengzhi Zhang
https://arxiv.org/abs/2509.07801

SciNLP: A Domain-Specific Benchmark for Full-Text Scientific Entity and Relation Extraction in NLP
Structured information extraction from scientific literature is crucial for capturing core concepts and emerging trends in specialized fields. While existing datasets aid model development, most focus on specific publication sections due to domain complexity and the high cost of annotating scientific texts. To address this limitation, we introduce SciNLP - a specialized benchmark for full-text entity and relation extraction in the Natural Language Processing (NLP) domain. The dataset comprises …

Design of Input-Output Observers for a Population of Systems with Bounded Frequency-Domain Variation using $DK$-iteration
Timothy Everett Adams, James Richard Forbes
https://arxiv.org/abs/2509.07201

Design of Input-Output Observers for a Population of Systems with Bounded Frequency-Domain Variation using $DK$-iteration
This paper proposes a linear input-output observer design methodology for a population of systems in which each observer uses knowledge of the linear time-invariant dynamics of the particular device. Observers are typically composed of a known model of the system and a correction mechanism to produce an estimate of the state. The proposed design procedure characterizes the variation within the population in the frequency domain and synthesizes a single robust correction filter. The correction f…

Conditioning on PDE Parameters to Generalise Deep Learning Emulation of Stochastic and Chaotic Dynamics
Ira J. S. Shokar, Rich R. Kerswell, Peter H. Haynes
https://arxiv.org/abs/2509.09599

Conditioning on PDE Parameters to Generalise Deep Learning Emulation of Stochastic and Chaotic Dynamics
We present a deep learning emulator for stochastic and chaotic spatio-temporal systems, explicitly conditioned on the parameter values of the underlying partial differential equations (PDEs). Our approach involves pre-training the model on a single parameter domain, followed by fine-tuning on a smaller, yet diverse dataset, enabling generalisation across a broad range of parameter values. By incorporating local attention mechanisms, the network is capable of handling varying domain sizes and re…

Overcoming DNSSEC Islands of Security: A TLS and IP-Based Certificate Solution
Aduma Rishith, Aditya Kulkarni, Tamal Das, Vivek Balachandran
https://arxiv.org/abs/2509.08364 htt…

Overcoming DNSSEC Islands of Security: A TLS and IP-Based Certificate Solution
The Domain Name System (DNS) serves as the backbone of the Internet, primarily translating domain names to IP addresses. Over time, various enhancements have been introduced to strengthen the integrity of DNS. Among these, DNSSEC stands out as a leading cryptographic solution. It protects against attacks (such as DNS spoofing) by establishing a chain of trust throughout the DNS nameserver hierarchy. However, DNSSEC's effectiveness is compromised when there is a break in this chain, resulting in…

Domain-Shift-Aware Conformal Prediction for Large Language Models
Zhexiao Lin, Yuanyuan Li, Neeraj Sarna, Yuanyuan Gao, Michael von Gablenz
https://arxiv.org/abs/2510.05566 http…

Domain-Shift-Aware Conformal Prediction for Large Language Models
Large language models have achieved impressive performance across diverse tasks. However, their tendency to produce overconfident and factually incorrect outputs, known as hallucinations, poses risks in real world applications. Conformal prediction provides finite-sample, distribution-free coverage guarantees, but standard conformal prediction breaks down under domain shift, often leading to under-coverage and unreliable prediction sets. We propose a new framework called Domain-Shift-Aware Conf…

Robust Source-Free Domain Adaptation for Medical Image Segmentation based on Curriculum Learning
Ziqi Zhang, Yuexiang Li, Yawen Huang, Nanjun He, Tao Xu, Liwei Lin, Yefeng Zheng, Shaoxin Li, Feiyue Huang
https://arxiv.org/abs/2510.08393

Robust Source-Free Domain Adaptation for Medical Image Segmentation based on Curriculum Learning
Recent studies have uncovered a new research line, namely source-free domain adaptation, which adapts a model to target domains without using the source data. Such a setting can address the concerns on data privacy and security issues of medical images. However, current source-free domain adaptation frameworks mainly focus on the pseudo label refinement for target data without the consideration of learning procedure. Indeed, a progressive learning process from source to target domain will benef…

malaria_genes: Malaria var DBLa HVR networks
Networks of recombinant antigen genes from the human malaria parasite P. falciparum. Each of the 9 networks shares the same set of vertices but has different edges, corresponding to the 9 highly variable regions (HVRs) in the DBLa domain of the var protein. Nodes are var genes, and two genes are connected if they share a substring whose length is statistically significant. Metadata includes two types of node labels, both based on sequence st…

AutoODD: Agentic Audits via Bayesian Red Teaming in Black-Box Models
Rebecca Martin, Jay Patrikar, Sebastian Scherer
https://arxiv.org/abs/2509.08638 https://

AutoODD: Agentic Audits via Bayesian Red Teaming in Black-Box Models
Specialized machine learning models, regardless of architecture and training, are susceptible to failures in deployment. With their increasing use in high risk situations, the ability to audit these models by determining their operational design domain (ODD) is crucial in ensuring safety and compliance. However, given the high-dimensional input spaces, this process often requires significant human resources and domain expertise. To alleviate this, we introduce \coolname, an LLM-Agent centric fr…

Handling Open-Vocabulary Constructs in Formalizing Specifications: Retrieval-Augmented Parsing with Expert Knowledge
Mohammad Saqib Hasan, Sayontan Ghosh, Dhruv Verma, Geoff Kuenning, Erez Zadok, Scott A. Smolka, Niranjan Balasubramanian
https://arxiv.org/abs/2509.08808

Handling Open-Vocabulary Constructs in Formalizing Specifications: Retrieval-Augmented Parsing with Expert Knowledge
We study the problem of Open-Vocabulary Constructs(OVCs) -- ones not known beforehand -- in the context of converting natural language (NL) specifications into formal languages (e.g., temporal logic or code). Models fare poorly on OVCs due to a lack of necessary knowledge a priori. In such situations, a domain expert can provide correct constructs at inference time based on their preferences or domain knowledge. Our goal is to effectively reuse this inference-time, expert-provided knowledge for…

Prepared mind, fast response: A temporal decoupling framework for adaptive knowledge orchestration in open-domain dialogue
Jinling Gan, Churong Liang, Runnan Li
https://arxiv.org/abs/2510.08175

Prepared mind, fast response: A temporal decoupling framework for adaptive knowledge orchestration in open-domain dialogue
The latency-quality tradeoff is a fundamental constraint in open-domain dialogue AI systems, since comprehensive knowledge access necessitates prohibitive response delays. Contemporary approaches offer two inadequate solutions: lightweight instruct models achieve sub-second latency but lack reasoning depth, while tool-augmented ReAct agents enhance factuality through external knowledge at the cost of synchronous execution that blocks interaction during re- trieval processes. PMFR is thus propos…

Retrieval-Augmented Generation for Reliable Interpretation of Radio Regulations
Zakaria El Kassimi, Fares Fourati, Mohamed-Slim Alouini
https://arxiv.org/abs/2509.09651 https://…

Retrieval-Augmented Generation for Reliable Interpretation of Radio Regulations
We study question answering in the domain of radio regulations, a legally sensitive and high-stakes area. We propose a telecom-specific Retrieval-Augmented Generation (RAG) pipeline and introduce, to our knowledge, the first multiple-choice evaluation set for this domain, constructed from authoritative sources using automated filtering and human validation. To assess retrieval quality, we define a domain-specific retrieval metric, under which our retriever achieves approximately 97% accuracy. B…

My best "rubber duck" is whatever public support fora exist for whatever I'm bloodying my forehead on. I rarely actually get useful responses, but just asking does something, sometimes.
e.g. https://forum.opennebula.io/t/debugging-domain-creation/14158/1

Debugging domain creation
Executive Summary: OpenNebula environment grown over 12 years running OpenNebula v5.2, upgrading host OS breaks initial domain creation. Live migration works! Versions of the related components and OS (frontend, hypervisors, VMs): CentOS7 with qemu-kvm on 30 Intel-cpu (Supermicro) hosts. some hosts upgraded to Alma9, fully updated. VMs a mix of versions of FreeBSD, CentOS, Alma, Ubuntu, Windows. CentOS7 hosts have been operating properly for many years. Some Alma9 hosts are new, others are…

SciGPT: A Large Language Model for Scientific Literature Understanding and Knowledge Discovery
Fengyu She, Nan Wang, Hongfei Wu, Ziyi Wan, Jingmian Wang, Chang Wang
https://arxiv.org/abs/2509.08032

SciGPT: A Large Language Model for Scientific Literature Understanding and Knowledge Discovery
Scientific literature is growing exponentially, creating a critical bottleneck for researchers to efficiently synthesize knowledge. While general-purpose Large Language Models (LLMs) show potential in text processing, they often fail to capture scientific domain-specific nuances (e.g., technical jargon, methodological rigor) and struggle with complex scientific tasks, limiting their utility for interdisciplinary research. To address these gaps, this paper presents SciGPT, a domain-adapted found…

malaria_genes: Malaria var DBLa HVR networks
Networks of recombinant antigen genes from the human malaria parasite P. falciparum. Each of the 9 networks shares the same set of vertices but has different edges, corresponding to the 9 highly variable regions (HVRs) in the DBLa domain of the var protein. Nodes are var genes, and two genes are connected if they share a substring whose length is statistically significant. Metadata includes two types of node labels, both based on sequence st…

Weekend #Plankton Factoid 🦠🦐
Heliozoans are a group of amoeboid protists found commonly in both fresh and saltwater. They were termed sun-animalcules due to their spherical shape and distinctive radiating microtubules, which support axiopods used to capture food and facilitate movement. Some will also capture symbiotic algal cells which provide energy through photosynthesis. Heliozoa is &quo…

GTA-Crime: A Synthetic Dataset and Generation Framework for Fatal Violence Detection with Adversarial Snippet-Level Domain Adaptation
Seongho Kim, Sejong Ryu, Hyoukjun You, Je Hyeong Hong
https://arxiv.org/abs/2509.08232

GTA-Crime: A Synthetic Dataset and Generation Framework for Fatal Violence Detection with Adversarial Snippet-Level Domain Adaptation
Recent advancements in video anomaly detection (VAD) have enabled identification of various criminal activities in surveillance videos, but detecting fatal incidents such as shootings and stabbings remains difficult due to their rarity and ethical issues in data collection. Recognizing this limitation, we introduce GTA-Crime, a fatal video anomaly dataset and generation framework using Grand Theft Auto 5 (GTA5). Our dataset contains fatal situations such as shootings and stabbings, captured fro…

Bringing Multi-Modal Multi-Task Federated Foundation Models to Education Domain: Prospects and Challenges
Kasra Borazjani, Naji Khosravan, Rajeev Sahay, Bita Akram, Seyyedali Hosseinalipour
https://arxiv.org/abs/2509.07946

Bringing Multi-Modal Multi-Task Federated Foundation Models to Education Domain: Prospects and Challenges
Multi-modal multi-task (M3T) foundation models (FMs) have recently shown transformative potential in artificial intelligence, with emerging applications in education. However, their deployment in real-world educational settings is hindered by privacy regulations, data silos, and limited domain-specific data availability. We introduce M3T Federated Foundation Models (FedFMs) for education: a paradigm that integrates federated learning (FL) with M3T FMs to enable collaborative, privacy-preserving…

The time-domain gamma-ray sky seen by the Fermi-LAT
Giacomo Principe
https://arxiv.org/abs/2509.07660 https://arxiv.org/pdf/2509.07660

The time-domain gamma-ray sky seen by the Fermi-LAT
The Fermi Gamma-ray Space Telescope is currently celebrating its 15th anniversary of operation. Since its launch, the Fermi-Large Area Telescope (LAT), the main instrument onboard the Fermi satellite, has remarkably unveiled the sky at GeV energies providing outstanding results in time-domain gamma-ray astrophysics. In particular, LAT has observed some of the most powerful transient phenomena in the Universe (such as gamma-ray bursts, blazar flares, magnetar flares, ...) enabling the possibilit…

Efficient Computation of Voronoi Diagrams Using Point-in-Cell Tests
Yanyang Xiao, Yao Li, Juan Cao, Zhonggui Chen
https://arxiv.org/abs/2509.07175 https://…

Efficient Computation of Voronoi Diagrams Using Point-in-Cell Tests
Since the Voronoi diagram appears in many applications, the topic of improving its computational efficiency remains attractive. We propose a novel yet efficient method to compute Voronoi diagrams bounded by a given domain, i.e., the clipped or restricted Voronoi diagrams. The intersection of the domain and a Voronoi cell (domain-cell intersection) is generated by removing the part outside the cell from the domain, which can be accomplished by several clippings. Different from the existing metho…

MIRANDA: short signatures from a leakage-free full-domain-hash scheme
Alain Couvreur, Thomas Debris-Alazard, Philippe Gaborit, Adrien Vin\c{c}otte
https://arxiv.org/abs/2510.07479

MIRANDA: short signatures from a leakage-free full-domain-hash scheme
We present $\mathsf{Miranda}$, the first family of full-domain-hash signatures based on matrix codes. This signature scheme fulfils the paradigm of Gentry, Peikert and Vaikuntanathan ($\mathsf{GPV}$), which gives strong security guarantees. Our trapdoor is very simple and generic: if we propose it with matrix codes, it can actually be instantiated in many other ways since it only involves a subcode of a decodable code (or lattice) in a unique decoding regime of parameters. Though $\mathsf{Miran…

Normalized solutions for fractional Choquard equation with critical growth on bounded domain
Divya Goel, Asmita Rai
https://arxiv.org/abs/2509.07618 https://

Normalized solutions for fractional Choquard equation with critical growth on bounded domain
In this work, we establish the multiplicity of positive solutions for the following critical fractional Choquard equation with a perturbation on the star-shaped bounded domain $$ \left\{ \begin{array}{lr} (-Δ)^s u = λu +α|u|^{p-2}u+ \left( \int\limits_Ω \frac{|u(y)|^{2^{*}_{μ,s}}}{|x-y|^ μ}\, dy\right) |u|^{2^{*}_{μ,s}-2}u\; \text{in} \; Ω,\\ u>0\; \text{in}\; Ω,\; \\ u = 0\; \text{in} \; \mathbb{R}^{N}\backslashΩ, \\ \int_Ω|u|^2 dx…

Unstructured to structured: geometric multigrid on complex geometries via domain remapping
Nicolas Nytko, Scott MacLachlan, J. David Moulton, Luke N. Olson, Andrew Reisner, Matthew West
https://arxiv.org/abs/2509.08109

Unstructured to structured: geometric multigrid on complex geometries via domain remapping
For domains that are easily represented by structured meshes, robust geometric multigrid solvers can quickly provide the numerical solution to many discretized elliptic PDEs. However, for complicated domains with unstructured meshes, constructing suitable hierarchies of meshes becomes challenging. We propose a framework for mapping computations from such complex domains to regular computational domains via diffeomorphisms, enabling the use of robust geometric-style multigrid. This mapping facil…

malaria_genes: Malaria var DBLa HVR networks
Networks of recombinant antigen genes from the human malaria parasite P. falciparum. Each of the 9 networks shares the same set of vertices but has different edges, corresponding to the 9 highly variable regions (HVRs) in the DBLa domain of the var protein. Nodes are var genes, and two genes are connected if they share a substring whose length is statistically significant. Metadata includes two types of node labels, both based on sequence st…

Domain-Specific Fine-Tuning and Prompt-Based Learning: A Comparative Study for developing Natural Language-Based BIM Information Retrieval Systems
Han Gao, Timo Hartmann, Botao Zhong, Kai Lia, Hanbin Luo
https://arxiv.org/abs/2508.05676

Domain-Specific Fine-Tuning and Prompt-Based Learning: A Comparative Study for developing Natural Language-Based BIM Information Retrieval Systems
Building Information Modeling (BIM) is essential for managing building data across the entire lifecycle, supporting tasks from design to maintenance. Natural Language Interface (NLI) systems are increasingly explored as user-friendly tools for information retrieval in Building Information Modeling (BIM) environments. Despite their potential, accurately extracting BIM-related data through natural language queries remains a persistent challenge due to the complexity use queries and specificity of…

In-Context Policy Adaptation via Cross-Domain Skill Diffusion
Minjong Yoo, Woo Kyung Kim, Honguk Woo
https://arxiv.org/abs/2509.04535 https://arxiv.org/pdf…

In-Context Policy Adaptation via Cross-Domain Skill Diffusion
In this work, we present an in-context policy adaptation (ICPAD) framework designed for long-horizon multi-task environments, exploring diffusion-based skill learning techniques in cross-domain settings. The framework enables rapid adaptation of skill-based reinforcement learning policies to diverse target domains, especially under stringent constraints on no model updates and only limited target domain data. Specifically, the framework employs a cross-domain skill diffusion scheme, where domai…

Integrating Domain Knowledge into Process Discovery Using Large Language Models
Ali Norouzifar, Humam Kourani, Marcus Dees, Wil van der Aalst
https://arxiv.org/abs/2510.07161 ht…

Integrating Domain Knowledge into Process Discovery Using Large Language Models
Process discovery aims to derive process models from event logs, providing insights into operational behavior and forming a foundation for conformance checking and process improvement. However, models derived solely from event data may not accurately reflect the real process, as event logs are often incomplete or affected by noise, and domain knowledge, an important complementary resource, is typically disregarded. As a result, the discovered models may lack reliability for downstream tasks. We…

Domain-Aware RAG: MoL-Enhanced RL for Efficient Training and Scalable Retrieval
Hao Lin, Peitong Xie, Jingxue Chen, Jie Lin, Qingkun Tang, Qianchun Lu
https://arxiv.org/abs/2509.06650

Domain-Aware RAG: MoL-Enhanced RL for Efficient Training and Scalable Retrieval
Retrieval-Augmented Generation (RAG) systems rely heavily on the retrieval stage, particularly the coarse-ranking process. Existing coarse-ranking optimization approaches often struggle to balance domain-specific knowledge learning with query enhencement, resulting in suboptimal retrieval performance. To address this challenge, we propose MoLER, a domain-aware RAG method that uses MoL-Enhanced Reinforcement Learning to optimize retrieval. MoLER has a two-stage pipeline: a continual pre-training…

Visual-TableQA: Open-Domain Benchmark for Reasoning over Table Images
Boammani Aser Lompo, Marc Haraoui
https://arxiv.org/abs/2509.07966 https://arxiv.org/…

Visual-TableQA: Open-Domain Benchmark for Reasoning over Table Images
Visual reasoning over structured data such as tables is a critical capability for modern vision-language models (VLMs), yet current benchmarks remain limited in scale, diversity, or reasoning depth, especially when it comes to rendered table images. Addressing this gap, we introduce Visual-TableQA, a large-scale, open-domain multimodal dataset specifically designed to evaluate and enhance visual reasoning over complex tabular data. Our generation pipeline is modular, scalable, and fully autonom…

Design and Implementation of a Domain-specific Language for Modelling Evacuation Scenarios Using Eclipse EMG/GMF Tool
Heerok Banerjee
https://arxiv.org/abs/2509.06688 https://…

Design and Implementation of a Domain-specific Language for Modelling Evacuation Scenarios Using Eclipse EMG/GMF Tool
Domain-specific languages (DSLs) play a crucial role in resolving internal dependencies across enterprises and boosts their upfront business management processes. Yet, a lot of development is needed to build modelling frameworks which support graphical interfaces (canvas, pallettes etc.), hierarchical structures and easy implementation to shorten the gap for novice users. In this paper, a DSL namely, Bmod is introduced, which can be used to model evacuation scenarios. The language is built usin…

Reconstructing the dielectric properties of melanoma in 3D using real-life melanoma model
Georg Kyhn, Eric Lindstr\"om, Larisa Beilina
https://arxiv.org/abs/2508.07780 http…

Reconstructing the dielectric properties of melanoma in 3D using real-life melanoma model
The paper presents performance of the adaptive domain decomposition finite element/finite difference method for reconstruction of the dielectric permittivity and conductivity functions for 3D real-life melanoma model using measurements of the backscattered electric field at the boundary of the investigated domain. We present several gradient-based reconstruction algorithms which use optimization approach to find stationary point of the Lagrangian. Our computational tests show qualitative and qu…

One View, Many Worlds: Single-Image to 3D Object Meets Generative Domain Randomization for One-Shot 6D Pose Estimation
Zheng Geng, Nan Wang, Shaocong Xu, Chongjie Ye, Bohan Li, Zhaoxi Chen, Sida Peng, Hao Zhao
https://arxiv.org/abs/2509.07978

One View, Many Worlds: Single-Image to 3D Object Meets Generative Domain Randomization for One-Shot 6D Pose Estimation
Estimating the 6D pose of arbitrary unseen objects from a single reference image is critical for robotics operating in the long-tail of real-world instances. However, this setting is notoriously challenging: 3D models are rarely available, single-view reconstructions lack metric scale, and domain gaps between generated models and real-world images undermine robustness. We propose OnePoseViaGen, a pipeline that tackles these challenges through two key components. First, a coarse-to-fine alignmen…

Incentivizing Safer Actions in Policy Optimization for Constrained Reinforcement Learning
Somnath Hazra, Pallab Dasgupta, Soumyajit Dey
https://arxiv.org/abs/2509.09208 https://…

Incentivizing Safer Actions in Policy Optimization for Constrained Reinforcement Learning
Constrained Reinforcement Learning (RL) aims to maximize the return while adhering to predefined constraint limits, which represent domain-specific safety requirements. In continuous control settings, where learning agents govern system actions, balancing the trade-off between reward maximization and constraint satisfaction remains a significant challenge. Policy optimization methods often exhibit instability near constraint boundaries, resulting in suboptimal training performance. To address t…

SKATE, a Scalable Tournament Eval: Weaker LLMs differentiate between stronger ones using verifiable challenges
Dewi S. W. Gould, Bruno Mlodozeniec, Samuel F. Brown
https://arxiv.org/abs/2508.06111

SKATE, a Scalable Tournament Eval: Weaker LLMs differentiate between stronger ones using verifiable challenges
Evaluating the capabilities and risks of foundation models is paramount, yet current methods demand extensive domain expertise, hindering their scalability as these models rapidly evolve. We introduce SKATE: a novel evaluation framework in which large language models (LLMs) compete by generating and solving verifiable tasks for one another. Our core insight is to treat evaluation as a game: models act as both task-setters and solvers, incentivized to create questions which highlight their own s…

DACIP-RC: Domain Adaptive Continual Instruction Pre-Training via Reading Comprehension on Business Conversations
Elena Khasanova, Harsh Saini, Md Tahmid Rahman Laskar, Xue-Yong Fu, Cheng Chen, Shashi Bhushan TN
https://arxiv.org/abs/2510.08152

DACIP-RC: Domain Adaptive Continual Instruction Pre-Training via Reading Comprehension on Business Conversations
The rapid advancements in Large Language Models (LLMs) have enabled their adoption in real-world industrial scenarios for various natural language processing tasks. However, the high inference cost of large-scale LLMs makes their deployment impractical, necessitating the use of smaller models. Despite their efficiency, smaller LLMs lack robust zero-shot instruction-following capabilities across diverse domains, limiting their adaptability to dynamic user requirements. Traditional fine-tuning ap…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/4]:
- Maximizing Information in Domain-Invariant Representation Improves Transfer Learning
Adrian Shuai Li, Elisa Bertino, Xuan-Hong Dang, Ankush Singla, Yuhai Tu, Mark N Wegman

Retrieval Augmented Large Language Model System for Comprehensive Drug Contraindications
Byeonghun Bang, Jongsuk Yoon, Dong-Jin Chang, Seho Park, Yong Oh Lee
https://arxiv.org/abs/2508.06145

Retrieval Augmented Large Language Model System for Comprehensive Drug Contraindications
The versatility of large language models (LLMs) has been explored across various sectors, but their application in healthcare poses challenges, particularly in the domain of pharmaceutical contraindications where accurate and reliable information is required. This study enhances the capability of LLMs to address contraindications effectively by implementing a Retrieval Augmented Generation (RAG) pipeline. Utilizing OpenAI's GPT-4o-mini as the base model, and the text-embedding-3-small model for…

interactome_pdz: PDZ-domain interactome (2005)
A network of PDZ-domain-mediated protein–protein binding interactions, extracted from the PDZBase database. Nodes represent proteins and an edge represents a binding interaction between two proteins.
This network has 212 nodes and 244 edges.
Tags: Biological, Protein interactions, Unweighted

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/3]:
- ToDMA: Large Model-Driven Token-Domain Multiple Access for Semantic Communications
Li Qiao, Mahdi Boloursaz Mashhadi, Zhen Gao, Robert Schober, Deniz G\"und\"uz

Harnessing Adaptive Topology Representations for Zero-Shot Graph Question Answering
Yanbin Wei, Jiangyue Yan, Chun Kang, Yang Chen, Hua Liu, James T. Kwok, Yu Zhang
https://arxiv.org/abs/2508.06345

Harnessing Adaptive Topology Representations for Zero-Shot Graph Question Answering
Large Multimodal Models (LMMs) have shown generalized zero-shot capabilities in diverse domain question-answering (QA) tasks, including graph QA that involves complex graph topologies. However, most current approaches use only a single type of graph representation, namely Topology Representation Form (TRF), such as prompt-unified text descriptions or style-fixed visual styles. Those "one-size-fits-all" approaches fail to consider the specific preferences of different models or tasks, often lead…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/5]:
- ToDMA: Large Model-Driven Token-Domain Multiple Access for Semantic Communications
Li Qiao, Mahdi Boloursaz Mashhadi, Zhen Gao, Robert Schober, Deniz G\"und\"uz

Adaptive Stain Normalization for Cross-Domain Medical Histology
Tianyue Xu, Yanlin Wu, Abhai K. Tripathi, Matthew M. Ippolito, Benjamin D. Haeffele
https://arxiv.org/abs/2510.06592

Adaptive Stain Normalization for Cross-Domain Medical Histology
Deep learning advances have revolutionized automated digital pathology analysis. However, differences in staining protocols and imaging conditions can introduce significant color variability. In deep learning, such color inconsistency often reduces performance when deploying models on data acquired under different conditions from the training data, a challenge known as domain shift. Many existing methods attempt to address this problem via color normalization but suffer from several notable dra…

Physics-Informed Multimodal Bearing Fault Classification under Variable Operating Conditions using Transfer Learning
Tasfiq E. Alam, Md Manjurul Ahsan, Shivakumar Raman
https://arxiv.org/abs/2508.07536

Physics-Informed Multimodal Bearing Fault Classification under Variable Operating Conditions using Transfer Learning
Accurate and interpretable bearing fault classification is critical for ensuring the reliability of rotating machinery, particularly under variable operating conditions where domain shifts can significantly degrade model performance. This study proposes a physics-informed multimodal convolutional neural network (CNN) with a late fusion architecture, integrating vibration and motor current signals alongside a dedicated physics-based feature extraction branch. The model incorporates a novel physi…

Agentic LLMs for Question Answering over Tabular Data
Rishit Tyagi, Mohit Gupta, Rahul Bouri
https://arxiv.org/abs/2509.09234 https://arxiv.org/pdf/2509.09…

Agentic LLMs for Question Answering over Tabular Data
Question Answering over Tabular Data (Table QA) presents unique challenges due to the diverse structure, size, and data types of real-world tables. The SemEval 2025 Task 8 (DataBench) introduced a benchmark composed of large-scale, domain-diverse datasets to evaluate the ability of models to accurately answer structured queries. We propose a Natural Language to SQL (NL-to-SQL) approach leveraging large language models (LLMs) such as GPT-4o, GPT-4o-mini, and DeepSeek v2:16b to generate SQL queri…

Semi-supervised Deep Transfer for Regression without Domain Alignment
Mainak Biswas, Ambedkar Dukkipati, Devarajan Sridharan
https://arxiv.org/abs/2509.05092 https://

Semi-supervised Deep Transfer for Regression without Domain Alignment
Deep learning models deployed in real-world applications (e.g., medicine) face challenges because source models do not generalize well to domain-shifted target data. Many successful domain adaptation (DA) approaches require full access to source data. Yet, such requirements are unrealistic in scenarios where source data cannot be shared either because of privacy concerns or because it is too large and incurs prohibitive storage or computational costs. Moreover, resource constraints may limit th…

AdsQA: Towards Advertisement Video Understanding
Xinwei Long, Kai Tian, Peng Xu, Guoli Jia, Jingxuan Li, Sa Yang, Yihua Shao, Kaiyan Zhang, Che Jiang, Hao Xu, Yang Liu, Jiaheng Ma, Bowen Zhou
https://arxiv.org/abs/2509.08621

AdsQA: Towards Advertisement Video Understanding
Large language models (LLMs) have taken a great step towards AGI. Meanwhile, an increasing number of domain-specific problems such as math and programming boost these general-purpose models to continuously evolve via learning deeper expertise. Now is thus the time further to extend the diversity of specialized applications for knowledgeable LLMs, though collecting high quality data with unexpected and informative tasks is challenging. In this paper, we propose to use advertisement (ad) videos a…

Towards EnergyGPT: A Large Language Model Specialized for the Energy Sector
Amal Chebbi, Babajide Kolade
https://arxiv.org/abs/2509.07177 https://arxiv.org…

Towards EnergyGPT: A Large Language Model Specialized for the Energy Sector
Large Language Models have demonstrated impressive capabilities across various domains. However, their general-purpose nature often limits their effectiveness in specialized fields such as energy, where deep technical expertise and precise domain knowledge are essential. In this paper, we introduce EnergyGPT, a domain-specialized language model tailored for the energy sector, developed by fine-tuning LLaMA 3.1-8B model using Supervised Fine-Tuning on a high-quality, curated corpus of energy-rel…

SAFT: Shape and Appearance of Fabrics from Template via Differentiable Physical Simulations from Monocular Video
David Stotko, Reinhard Klein
https://arxiv.org/abs/2509.08828 ht…

SAFT: Shape and Appearance of Fabrics from Template via Differentiable Physical Simulations from Monocular Video
The reconstruction of three-dimensional dynamic scenes is a well-established yet challenging task within the domain of computer vision. In this paper, we propose a novel approach that combines the domains of 3D geometry reconstruction and appearance estimation for physically based rendering and present a system that is able to perform both tasks for fabrics, utilizing only a single monocular RGB video sequence as input. In order to obtain realistic and high-quality deformations and renderings, …

LLM Ensemble for RAG: Role of Context Length in Zero-Shot Question Answering for BioASQ Challenge
Dima Galat, Diego Molla-Aliod
https://arxiv.org/abs/2509.08596 https://

LLM Ensemble for RAG: Role of Context Length in Zero-Shot Question Answering for BioASQ Challenge
Biomedical question answering (QA) poses significant challenges due to the need for precise interpretation of specialized knowledge drawn from a vast, complex, and rapidly evolving corpus. In this work, we explore how large language models (LLMs) can be used for information retrieval (IR), and an ensemble of zero-shot models can accomplish state-of-the-art performance on a domain-specific Yes/No QA task. Evaluating our approach on the BioASQ challenge tasks, we show that ensembles can outperfor…

PeftCD: Leveraging Vision Foundation Models with Parameter-Efficient Fine-Tuning for Remote Sensing Change Detection
Sijun Dong, Yuxuan Hu, LiBo Wang, Geng Chen, Xiaoliang Meng
https://arxiv.org/abs/2509.09572

PeftCD: Leveraging Vision Foundation Models with Parameter-Efficient Fine-Tuning for Remote Sensing Change Detection
To tackle the prevalence of pseudo changes, the scarcity of labeled samples, and the difficulty of cross-domain generalization in multi-temporal and multi-source remote sensing imagery, we propose PeftCD, a change detection framework built upon Vision Foundation Models (VFMs) with Parameter-Efficient Fine-Tuning (PEFT). At its core, PeftCD employs a weight-sharing Siamese encoder derived from a VFM, into which LoRA and Adapter modules are seamlessly integrated. This design enables highly effici…

Culturally transmitted color categories in LLMs reflect a learning bias toward efficient compression
Nathaniel Imel, Noga Zaslavsky
https://arxiv.org/abs/2509.08093 https://

Culturally transmitted color categories in LLMs reflect a learning bias toward efficient compression
Converging evidence suggests that systems of semantic categories across human languages achieve near-optimal compression via the Information Bottleneck (IB) complexity-accuracy principle. Large language models (LLMs) are not trained for this objective, which raises the question: are LLMs capable of evolving efficient human-like semantic systems? To address this question, we focus on the domain of color as a key testbed of cognitive theories of categorization and replicate with LLMs (Gemini 2.0-…

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation
Wenjun Yu, Yinchen Zhou, Jia-Xuan Jiang, Shubin Zeng, Yuee Li, Zhong Wang
https://arxiv.org/abs/2509.08570

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation
Multimodal models have achieved remarkable success in natural image segmentation, yet they often underperform when applied to the medical domain. Through extensive study, we attribute this performance gap to the challenges of multimodal fusion, primarily the significant semantic gap between abstract textual prompts and fine-grained medical visual features, as well as the resulting feature dispersion. To address these issues, we revisit the problem from the perspective of semantic aggregation. S…

TigerCoder: A Novel Suite of LLMs for Code Generation in Bangla
Nishat Raihan, Antonios Anastasopoulos, Marcos Zampieri
https://arxiv.org/abs/2509.09101 https://

TigerCoder: A Novel Suite of LLMs for Code Generation in Bangla
Despite being the 5th most spoken language, Bangla remains underrepresented in Large Language Models (LLMs), particularly for code generation. This primarily stems from the scarcity of high-quality data to pre-train and/or finetune such models. Hence, we introduce the first dedicated family of Code LLMs for Bangla (1B & 9B). We offer three major contributions: (1) a comprehensive Bangla code instruction datasets for programming domain adaptation; (2) MBPP-Bangla, an evaluation benchmark for Ban…

UniVideo: Unified Understanding, Generation, and Editing for Videos
Cong Wei, Quande Liu, Zixuan Ye, Qiulin Wang, Xintao Wang, Pengfei Wan, Kun Gai, Wenhu Chen
https://arxiv.org/abs/2510.08377

UniVideo: Unified Understanding, Generation, and Editing for Videos
Unified multimodal models have shown promising results in multimodal content generation and editing but remain largely limited to the image domain. In this work, we present UniVideo, a versatile framework that extends unified modeling to the video domain. UniVideo adopts a dual-stream design, combining a Multimodal Large Language Model (MLLM) for instruction understanding with a Multimodal DiT (MMDiT) for video generation. This design enables accurate interpretation of complex multimodal instru…

Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception
Spyridon Loukovitis, Anastasios Arsenos, Vasileios Karampinis, Athanasios Voulodimos
https://arxiv.org/abs/2509.09297

Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception
Open-set detection is crucial for robust UAV autonomy in air-to-air object detection under real-world conditions. Traditional closed-set detectors degrade significantly under domain shifts and flight data corruption, posing risks to safety-critical applications. We propose a novel, model-agnostic open-set detection framework designed specifically for embedding-based detectors. The method explicitly handles unknown object rejection while maintaining robustness against corrupted flight data. It e…

An Implemention of Two-Phase Image Segmentation using the Split Bregman Method
Olakunle S. Abawonse, G\"unay Do\u{g}an
https://arxiv.org/abs/2508.06351 https://

An Implemention of Two-Phase Image Segmentation using the Split Bregman Method
In this paper, we describe an implementation of the two-phase image segmentation algorithm proposed by Goldstein, Bresson, Osher in \cite{gold:bre}. This algorithm partitions the domain of a given 2d image into foreground and background regions, and each pixel of the image is assigned membership to one of these two regions. The underlying assumption for the segmentation model is that the pixel values of the input image can be summarized by two distinct average values, and that the region bounda…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/3]:
- Maximizing Information in Domain-Invariant Representation Improves Transfer Learning
Adrian Shuai Li, Elisa Bertino, Xuan-Hong Dang, Ankush Singla, Yuhai Tu, Mark N Wegman

Frequency Prior Guided Matching: A Data Augmentation Approach for Generalizable Semi-Supervised Polyp Segmentation
Haoran Xi, Chen Liu, Xiaolin Li
https://arxiv.org/abs/2508.06517

Frequency Prior Guided Matching: A Data Augmentation Approach for Generalizable Semi-Supervised Polyp Segmentation
Automated polyp segmentation is essential for early diagnosis of colorectal cancer, yet developing robust models remains challenging due to limited annotated data and significant performance degradation under domain shift. Although semi-supervised learning (SSL) reduces annotation requirements, existing methods rely on generic augmentations that ignore polyp-specific structural properties, resulting in poor generalization to new imaging centers and devices. To address this, we introduce Frequen…

Med-GRIM: Enhanced Zero-Shot Medical VQA using prompt-embedded Multimodal Graph RAG
Rakesh Raj Madavan, Akshat Kaimal, Hashim Faisal, Chandrakala S
https://arxiv.org/abs/2508.06496

Med-GRIM: Enhanced Zero-Shot Medical VQA using prompt-embedded Multimodal Graph RAG
An ensemble of trained multimodal encoders and vision-language models (VLMs) has become a standard approach for visual question answering (VQA) tasks. However, such models often fail to produce responses with the detailed precision necessary for complex, domain-specific applications such as medical VQA. Our representation model, BIND: BLIVA Integrated with Dense Encoding, extends prior multimodal work by refining the joint embedding space through dense, query-token-based encodings inspired by c…

Retrieval-Augmented VLMs for Multimodal Melanoma Diagnosis
Jihyun Moon, Charmgil Hong
https://arxiv.org/abs/2509.08338 https://arxiv.org/pdf/2509.08338

Retrieval-Augmented VLMs for Multimodal Melanoma Diagnosis
Accurate and early diagnosis of malignant melanoma is critical for improving patient outcomes. While convolutional neural networks (CNNs) have shown promise in dermoscopic image analysis, they often neglect clinical metadata and require extensive preprocessing. Vision-language models (VLMs) offer a multimodal alternative but struggle to capture clinical specificity when trained on general-domain data. To address this, we propose a retrieval-augmented VLM framework that incorporates semantically…

You Share Beliefs, I Adapt: Progressive Heterogeneous Collaborative Perception
Hao Si, Ehsan Javanmardi, Manabu Tsukada
https://arxiv.org/abs/2509.09310 https://

You Share Beliefs, I Adapt: Progressive Heterogeneous Collaborative Perception
Collaborative perception enables vehicles to overcome individual perception limitations by sharing information, allowing them to see further and through occlusions. In real-world scenarios, models on different vehicles are often heterogeneous due to manufacturer variations. Existing methods for heterogeneous collaborative perception address this challenge by fine-tuning adapters or the entire network to bridge the domain gap. However, these methods are impractical in real-world applications, as…

DUDE: Diffusion-Based Unsupervised Cross-Domain Image Retrieval
Ruohong Yang, Peng Hu, Yunfan Li, Xi Peng
https://arxiv.org/abs/2509.04193 https://arxiv.or…

DUDE: Diffusion-Based Unsupervised Cross-Domain Image Retrieval
Unsupervised cross-domain image retrieval (UCIR) aims to retrieve images of the same category across diverse domains without relying on annotations. Existing UCIR methods, which align cross-domain features for the entire image, often struggle with the domain gap, as the object features critical for retrieval are frequently entangled with domain-specific styles. To address this challenge, we propose DUDE, a novel UCIR method building upon feature disentanglement. In brief, DUDE leverages a text-…